Integration of 3D seismic attributes and well logs for Asmari reservoir characterization in the Ramshir oilfield, the Dezful Embayment, SW Iran

Document Type : Research Paper

Authors

1 Department of Geology, Faculty of Sciences, Ferdowsi University , Mashhad, Iran

2 Earth Science Department Faculty of Natural Science, University of Tabriz, Iran

3 Research Institute of Petroleum Industries (RIPI), Tehran, Iran

4 National Iranian South Oil Company (NISOC), Geophysics Department, Ahvaz, Iran

Abstract

3D seismic attributes and well logs were used to estimated porosity and water saturation in the Asmari
Formation in the Dezful Embayment, SW Iran. For this purpose, at first, the 3D seismic volume was
inverted base on the model, to obtain acoustic impedance cube. Afterward, the impedance and other
attributes extracted from seismic volume were analyzed by multiple attribute regression transform and
neural networks to predict porosity and water saturation between wells. Then linear or non–linear
combinations of attributes performed for porosity and water saturation prediction. The result shows that
the match between the actual and predicted porosity and water saturation improved; using only a single
attribute to the derived multi attribute transforms and neural networks model. Based on the results of
neural networks, the highest cross–correlation was observed between seismic attributes and the observed
target logs between seven wells in the study area. Based on our study, the cross–correlation between
actual and predicted porosity and water saturation increased and reached 93% and 90% respectively in
the case of using probabilistic neural networks (PNN). Finally, according to the cross–validation results,
PNN neural networks are used for porosity and water saturation prediction. We carry out porosity and
water saturation slicing from the Asmari Formation for display lateral and vertical heterogeneities, and
the result provided a reliable picture from subsurface formations. Porosity maps distribution shows the
western portion of the structure is highly porous and should be considered for further exploration and
development purposes. A possible reason for this high porosity in the western portion of the studied
formation is the presence of sand layers, especially in zone 2.Note that sand volume increased towards
west and northwest in direction of shadegan and Ahvaz fields and decreased towards east and southeast
to Rag–e–Sefid field. Based on the result between acoustic impedance and core, changes in acoustic
impedance were related to changes in the geological nature of the Asmari reservoir in the field.
Accordingly, seismic inversion is a powerful tool for studying the details of lithology and sedimentary
facies.

Keywords

Article Title [Persian]

-

References

Abreu, V., Sullivan, M., Pirmez, C., Mohrig, D., 2003. Lateral accretion packages (LAPs): an important reservoir element in deep water sinuous channels. Marine and Petroleum Geology, 20: 631- 648.##
Aleardi, M., 2018. Applying a probabilistic seismic-petrophysical inversion and two different rock
physics models for reservoir characterization in offshore Nile delta. Geophysics, 148: 272-286.##
Bosch, M., Mukerji, T., Gonzalez, E.F., 2010. Seismic inversion for reservoir properties combining statistical rock physics and geostatistics: A review. Geophysics, 75: 165-176.##
Calderon, J.E., Castagna, J., 2007. Porosity and lithologic estimation using rock physics and multiattribute transforms in Balcon Field, Colombia. The Leading Edge, 26: 142-150.##
Chen, Q., Sidney, S., 1997. Seismic attribute technology for reservoir forecasting and monitoring. The Leading Edge, 16: 445-456.##
Chopra, S., Marfurt, K. J., 2007. Seismic Attributes for Prospect Identification and Reservoir
Geopersia 2021, 11(1): 1-21 19##
Characterization: Geophysical Development Series 11, Society of Exploration Geophysics, 481 p.##
Cooke, D., Schneider, W.A., 1983. Generalized linear inversion of reflection seismic data. Geophysics,48: 665-676.##
Cooke, D., Cant, J., 2010. Model-based Seismic Inversion: Comparing deterministic and probabilistic approaches. Canadian Society of Exploration. Geophysicists, CSEG Recorder, 35: 28-39.##
Faraji, M. A., Kadkhodaie, A., Rezaee, R., Wood, D. A., 2017. Integration of core data, well logs and seismic attributes for identification of the low reservoir quality units with unswept gas in the
carbonate rocks of the world’s largest gas field. Journal of Earth Science, 28: 857-866.##
Farfour, M., Yoon, W.J., Kim, J., 2015. Seismic attributes and acoustic impedance inversion in
interpretation of complex hydrocarbon reservoirs. Journal of Applied Geophysics, 114: 68-80.##
Gogoi, T., Chatterjee, R., 2019. Estimation of petrophysical parameters using seismic inversion and neural network modeling in upper Assam Basin, India, Geoscience Frontiers, 10(3):1113-1124.##
Hampson, D.P., Schuelke, J.S., Quirein, J.A., 2001. Use of multiattribute transforms to predict log properties from seismic data. Geophysics, 66:220-236.##
Haris, A., Pradana, G. S., Riyanto, A., 2017.Combination of Seismic Inversion and Spectral
Decomposition to Identify Reservoir and Gas Content: A Case Study of CH Field, East Java Basin AIP Conference Proceedings 1862, 030163.##
Huuse, M., Feary, D.A., 2005. Seismic inversion for acoustic impedance and porosity of Cenozoic coolwater carbonates on the upper continental slope of the Great Australian Bight. Marine Geology, 215:123-134.##
Iturrarán-Viveros, U., Parra, J.O., 2014. Artificial Neural Networks applied to estimate permeability, porosity and intrinsic attenuation using seismic attributes and well log data. Journal of Applied Geophysics, 107:45-54.##
John, A., Lake, L.W., Torres-Verdin, C., Srinivasan, 2005. Seismic Facies Identification and
classification using simple statistics. Society of Petroleum Engineers. SPE 96577.##
Kadkhodaie-Ilkhchi, A., Rezaee, M.R., Rahimpour-Bonab, H., Chehrazi, A., 2009. Petrophysical data prediction from seismic attributes using committee fuzzy inference system. Computers &
Geosciences, 35: 2314-2330.##
Kadkhodaie-Ilkhchi, R., Moussavi-Harami, R., Rezaee, R., Nabi-Bidhendi, M., Kadkhodaie Ilkhchi, A., 2014. Seismic inversion and attributes analysis for porosity evaluation of the tight gas sandstones of the Whicher Range field in the Perth Basin, Western Australia. Journal of Natural Gas Science and Engineering, 21: 1073-1083.##
Kadkhodaie-Ilkhchi, R., Rezaee, R., Moussavi-Harami, R., Kadkhodaie-Ilkhchi, A., 2013. Analysis of the reservoir electrofacies in the framework of hydraulic flow units in the Whicher Range Field, Perth Basin, Western Australia. Journal of Petroleum Science and Engineering, 111: 106-120.##
Khoshdel, H., Riahi, M.A., 2007. 3D porosity estimation using multi attribute analysis methods in one of the Persian Gulf oil fields. EUROPEC /EAGE Annual Conference and Exhibition, London, June 11-14: 1-12.##
Khoshdel, H., Riahi, M.A., 2011. Multi attribute transform and neural network in porosity estimation of an offshore oil field. A case study. Journal of Petroleum Science and Engineering, 78:740-747.##
Kumar, R., Das, B., Chatterjee, R., Sain, K., 2016. A methodology of porosity estimation from inversion of post stack seismic data. Journal of Natural Gas Science and Engineering, 28: 356-364.##
Lavergne, M., Willm, C., 1977. Inversion of seismograms and pseudo velocity logs. Geophysical Prospecting, 25: 231-250.##
Leiphart, D.J., Hart, B.S., 2001. Comparison of linear regression and a probabilistic neural network to predict porosity from 3D seismic attributes in Lower Brushy Canyon channeled sandstones, southeast New Mexico. Geophysics, 66: 1349-1358.##
Leite, E.P., Vidal, A.C., 2011. 3D porosity prediction from seismic inversion and neural networks. Computers & Geosciences, 37: 1174-1180.##
Li, M., 2014. Geophysical Exploration Technology: Applications in Lithological and Stratigraphic
Reservoirs (1rd edition). Elsevier Inc.MA. USA.480 p. Lindseth, R.O., 1979. Synthetic sonic logs: a process for stratigraphic interpretation. Geophysics, 44: 3-26##
Mallick, S., 1995. Model based inversion of amplitude variations with offset data using a genetic algorithm. Geophysics, 60: 939-954.##
20 Sadeghi et al. Maurya, S.P., Singh, K.H., 2019. Predicting Porosity by Multivariate Regression and Probabilistic Neural Network using Model based and Coloured Inversion as External Attributes: A Quantitative Comparison. Journal of Geological Society of India, 93: 207-212.##
Naeem, M., El-Araby, H.M., Khalil, M.K., Jafri, M.K., Khan, F., 2015. Integrated study of seismic and well data for porosity estimation using multi attribute transforms: a case study of Boonsville Field, Fort Worth Basin, Texas, USA. Arabian Journal of Geoscience, 8: 8777-8793.##
Na’imi, S.R., Shadizadeh, S.R., Riahi, M.A., Mirzakhanian, M., 2014. Estimation of reservoir porosity and water saturation based on seismic attributes using support vector regression approach. Journal of Applied Geophysics, 107: 93-101.##
NISOC, 2016. Map of distribution of oil and gas fields in the Zagros basin, IranNational .(50,000/1) ,Iranian South Oil Company.(unpublished).Ahwaz, Iran Ogiesoba, O.C., 2010. Porosity prediction from seismic attributes of the Ordovician Trenton Black River groups, Rochester field, southern Ontario. American Association of Petroleum Geologists Bulletin,
94:1673-1693.##
Osleger, D.A., Montanez, I.P., 1996. Cross-platform architecture of a sequence boundary in mixed siliciclastic-carbonate lithofacies, Middle Cambrian, southern Great Basin, USA. Sedimentology, 43: 197-217.##
Perez-Muoz, T., Velasco-Hernandez, J., Hernandez-Martinez, E., 2013. Wavelet transform analysis for lithological characteristics identification in siliciclastic oil fields. Journal of Applied Geophysics, 98:298-308.##
Pramanik, A.G., Singh, V., Vig, R., Srivastava, A.K., Tiwary, D.N., 2004. Estimation of effective
porosity using geostatistics and multiattribute transforms: A case study. Geophysics, 69: 352-372.##
Prospectiuni., S.A. & Pedex, 2009. Final report on 3D land seismic acquisition Ramshir area, National Iranian South Oil Company (NISOC), Iran.##
Raeesi, M., Moradzadeh, A., Ardejani, F.D., Rahimi, M., 2012. Classification and identification of hydrocarbon reservoir lithofacies and their heterogeneity using seismic attributes, logs data, and artificial neural networks. Journal of Petroleum Science and Engineering, 82:151-165.##
Rezaee, M.R., Slatt, R., Kadkhodaie, A., 2006. Application of Intelligent Systems for Generating
Wireline Logs. Earth Science Journal, University of Oklahoma, USA, 2006 Edition. P.56-66.##
Rezvandehy, M., Aghababaei, H., Tabatabaee Raissi, S.H., 2011. Integrating seismic attributes in the accurate modeling of geological structures and determining the storage of the gas reservoir in Gorgan Plain (North of Iran). Journal of Applied Geophysics, 73: 187-195.##
Russell, B.H., 2004. The application of multivariate statistics and neural networks to the prediction of reservoir parameters using seismic attributes. Ph.D. Dissertation. University of Calgary, Alberta. 392p.##
Russell, B., Hampson, D., 1991. A comparison of post stack seismic inversion methods: In 61st Annual international Meeting, Society of Exploration Geophysicists, Expanded abstracts, pp. 876-878.##
Saltzer, R., Finn, C., Burtz, O., 2005. Predicting V shale and porosity using cascaded seismic and rock physics inversion. The Leading Edge, 24: 732-736.##
Schlumberger, 2003. A geological overview of Iran. Reservoir optimization conference, 19 p.
Sepehr, M., Cosgrove, J.W., 2005. The Role of the Kazerun fault zones in the formation and deformation of the Zagros fold-thrust belt, Iran. Tectonics, 24: 1-13.##
Schultz, P. S., Ronen, S., Hattori, M., Corbett, C., 1990a. Seismic guided estimation of log properties, part 1: The Leading Edge, 11: 543 - 514.##
Schultz, P. S., Ronen, S., Hattori, M., Corbett, C., 1990b. Seismic guided estimation of log properties, part 2: The Leading Edge, 11: 670-678##
Simm, R., Bacon, M., 2014. Seismic Amplitude: An interpreter’s handbook (1rd edition). Cambridge University Press, 281 p.##
Snedden, J.W., 2013. Channel-body basal scours: Observations from 3D seismic and importance for subsurface reservoir connectivity. Marine and Petroleum Geology, 39: 150-163.##
Specht, D.F., 1990. Probabilistic neural networks. Neural networks, 3: 109-118.##
Swisi A., 2009. Post-and pre-stack attribute analysis and inversion of Blackfoot 3D seismic dataset.M.Sc. thesis, Department of Geological Science, University of Saskatchewan.154p.##
Tonn, R., 2002. Neural network seismic reservoir characterization in heavy oil reservoir. The Leading Edge, 21: 309-312.##
Walls, J.D., Taner, M.T., Taylor, G., Smith, M., Carr, M., Derzhi, N., Drummond, J., McGuire, D.,
Morris, S., Bregar, J., 2002. Seismic reservoir characterization of a US Midcontinent fluvial system using rock physics, poststack seismic attributes, and neural networks. The Leading Edge, 21: 428- 436.##
Yao, T., Journel, A.G., 2000. Integrating seismic attribute maps and well logs for porosity modeling in a west Texas carbonate reservoir: addressing the scale and precision problem. Journal of Petroleum Science and Engineering, 28: 65-79.##

Files

History

  • Receive Date: 10 January 2020
  • Revise Date: 25 April 2020
  • Accept Date: 02 June 2020
  • First Publish Date: 01 January 2021

Share

How to cite

Statistics